Demodulation of data transmitted over time-varying channels with a free running hidden Markov state, like the phase noise channel or the fading channel, requires that the receiver tracks the hidden channel state. The tracking technique adopted in the paper is based on non-data-aided sequential importance sampling, also known as particle filtering.

The paper proposes a new particle filtering framework for data communication receivers based on an importance distribution such that each individual particle becomes a decision-directed Kalman filter relying upon its local symbol-by-symbol hard decisions. In this framework, different particles are left free to take different sequences of decisions. This leaves to the receiver the possibility of exploring different sequences of transmitted modulation symbols. The weight of the particle will be high for those particles that took in the past the correct sequence of decisions, while will be low for those particles that took wrong decisions. In the resampling procedure, particles with high weight will survive, while particles with low weight will be terminated, leaving space to the birth of new particles resampled from the surviving ones.

The crucial point in importance sampling is the choice of the importance distribution and the main novelty of the paper is the proposal of an importance distribution such that the particles of the particle filter become decision-directed Kalman filters. One important benefit brought by our proposed method is that, being non-data-aided, it does not need pilot symbols, thus allowing to preserve the transmission rate. A significant application example, presented and developed in the paper, is constituted by MIMO systems affected by phase noise, where the channel state vector consists of many parameters.

在具有自由运行的隐藏马尔可夫状态的时变通道上传输的数据的解调，如相位噪声通道或衰落通道，需要接收器跟踪隐藏通道状态。论文采用的跟踪技术是基于非数据辅助的顺序重要性采样，也称为粒子滤波。

该论文提出了一种新的基于重要性分布的数据通信接收器粒子滤波框架，使得每个单独的粒子成为依赖于其局部逐符号硬决策的决策导向卡尔曼滤波器。在这个框架中，不同的粒子可以自由地做出不同的决策序列。这为接收器留下了探索不同发射调制符号序列的可能性。对于那些在过去做出正确决策序列的粒子，粒子的权重会很高，而对于那些做出错误决策的粒子，权重会很低。在重采样过程中，高权重的粒子将存活下来，而低权重的粒子将被终止，为从幸存粒子中重采样的新粒子的诞生留出空间。

重要性采样的关键是重要性分布的选择，本文的主要新颖之处在于提出了一种重要性分布，使得粒子滤波器的粒子成为决策导向的卡尔曼滤波器。我们提出的方法带来的一个重要好处是，由于没有数据辅助，它不需要导频符号，因此可以保持传输速率。本文提出和开发的一个重要应用示例由受相位噪声影响的 MIMO 系统构成，其中信道状态向量由许多参数组成。